Cover image for Statistical strategies for small sample research
Title:
Statistical strategies for small sample research
Author:
Hoyle, Rick H.
Publication Information:
Thousand Oaks, Calif. : Sage Publications, [1999]

©1999
Physical Description:
xxi, 367 pages : illustrations ; 24 cm
Language:
English
Added Author:
ISBN:
9780761908852

9780761908869
Format :
Book

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HA29 .S7844 1999 Adult Non-Fiction Central Closed Stacks
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Summary

Summary

Newer statistical models, such as structural equation modeling and hierarchical linear modeling, require large sample sizes inappropriate for many research questions or unrealistic for many research arenas. How can researchers get the sophistication and flexibility of large sample studies without the requirement of prohibitively large samples? This book describes and illustrates statistical strategies that meet the sophistication/flexibility criteria for analyzing data from small samples of fewer than 150 cases. Contributions from some of the leading researchers in the field cover the use of multiple imputation software and how it can be used profitably with small data sets and missing data; ways to increase statistical power when sample size cannot be increased; and strategies for computing effect sizes and combining effect sizes across studies. Other contributions describe how to hypothesis test using the bootstrap; methods for pooling effect size indicators from single-case studies; frameworks for drawing inferences from cross-tabulated data; how to determine whether a correlation or covariance matrix warrants structure analysis; and what conditions indicate latent variable modeling is a viable approach to correct for unreliability in the mediator. Other topics include the use of dynamic factor analysis to model temporal processes by analyzing multivariate; time-series data from small numbers of individuals; techniques for coping with estimation problems in confirmatory factor analysis in small samples; how the state space model can be used with surprising accuracy with small data samples; and the use of partial least squares as a viable alternative to covariance-based SEM when the N is small and/or the number of variables in a model is large.


Author Notes

Wai Chan is Assistant Professor of Psychology at the Chinese University of Hong Kong
Wynne W. Chin is Associate Professor in the College of Business Administration at the University of Houston
Rachel T. Fouladi is Assistant Professor of Educational Psychology at the University of Texas at Austin
John W. Graham is Professor of Biobehavioral Health at Pennsylvania State University
Samuel B. Green is Professor and Chair of the Department of Psychology and Research in the School of Education at the University of Kansas
Kit-Tai Hau is Lecturer on the Faculty of Education at the Chinese University of Hong Kong
Dominique M. A. Haughton is Associate Professor of Statistics in the Mathematical Sciences Department at Bentley College
Scott L. Hershberger is Assistant Professor of Quantitative Psychology in the Department of Psychology at the University of Kansas
Rick H. Hoyle is Associate Professor of Psychology and Director of Methodology and Statistics at the Center for Prevention Research at the University of Kentucky
Robert A. R. G. Jansen is Statistical Researcher at the Department of Trade, Transport and Services of the Central Statistical Office, The Netherlands
David A. Kenny is Professor of Psychology at the University of Connecticut
Sharon H. Kramer is a doctoral candidate in the Department of Psychology at Harvard University
Janet G. Marquis is Director of the Research Design and Analysis Unit in the Institute for Life Span Studies at the University of Kansas
Herbert W. Marsh is Research Professor of Education at the University of Western Sydney-Macarthur in Australia
Scott E. Maxwell is Professor of Psychology at the University of Notre Dame
Cyrus R. Mehta is Adjunct Associate Professor in the Department of Biostatistics at the Harvard School of Public Health
Peter C. M. Molenaar is Professor of Psychology at the University of Amsterdam
John R. Nesselroade is Hugh Scott Hamilton Professor of Psychology at the University of Virginia
Peter R. Newsted is Professor of Management Information Systems at the University of Calgary
Johan H. L. Oud is Associate Professor of Longitudinal Assessment Methods in the Department of Special Education at the University of Nijmegen, The Netherlands
Nitin R. Patel is Vice President and cofounder of Cytel Software Corporation and Visiting Professor of Management Science at the Sloan School of Management at M.I.T.
Robert Rosenthal is Edgar Pierce Professor of Psychology at Harvard University
Joseph L. Schafer is Assistant Professor of Statistics at Pennsylvania State University
James H. Steiger is Professor of Psychology at the University of British Columbia
Anre Venter is Director of Undergraduate Studies in the Department of Psychology at the University of Notre Dame
Dennis D. Wallace is Assistant Professor of Biostatistics in the Department of Preventive Medicine at the University of Kansas Medical Center
Yiu-Fai Yung is Assistant Professor of Psychometrics at the University of North Carolina at Chapel Hill


Table of Contents

On the Performance of Multiple Imputation for Multivariate Data with Small Sample SizeJohn W Graham and Joseph L Schafer
Maximizing Power in Randomized Designs When N is SmallAnre Venter and Scott E Maxwell
Effect Sizes and Significance Levels in Small-Sample ResearchSharon H Kramer and Robert Rosenthal
Statistical Analysis Using BootstrappingYiu-Fai Yung and Wai Chan
Concepts and Implementation
Meta-Analysis of Single-Case DesignsScott L Hershberger et al
Exact Permutational Inference for Categorical and Nonparametric DataCyrus R Mehta and Nitin R Patel
Tests of an Identity Correlation StructureRachel T Fouladi and James H Steiger
Sample Size, Reliability and Tests of Statistical MediationRick H Hoyle and David A Kenny
Pooling Lagged Covariance Structures Based on Short, Multivariate Time Series for Dynamic Factor AnalysisJohn R Nesselroade and Peter C M Molenaar
Confirmatory Factor AnalysisHerbert W Marsh and Kit-Tai Hau
Strategies for Small Sample Sizes
Small Samples in Structural Equation State Space ModelingJohan H L Oud and Robert A R G Jansen and Dominique M A Haughton
Structural Equation Modeling Analysis with Small Samples Using Partial Least SquaresWynne W Chin and Peter R Newsted